直到这次字节的Seedance2.0出圈，很多人才第一次真正意识到，中国模型在 AI 视频这条赛道上，似乎 已不只是追赶，而是开始跑在前面了。 Seedance2.0不是靠某一帧画面惊艳出圈，而是带来了一种更隐蔽、却更深刻的变化，即AI 视频第一次 像一件可以被稳定交付的工业品。 多模态输入、自动运镜、长时一致性，这些能力叠加在一起，意味着创作者可以避免反复抽卡的痛苦， 而去推进一条可复用的生产流程。 但如果把时间线往前拨，会发现中国公司在AI视频的领先并不是突然发生的。 其实更早之前，中国模型在 AI 视频领域已获得了清晰的领先窗口。 例如去年4月的快手可灵2.0，文生视频对比Sora胜负比达367%，在人物一致性、生成稳定性与复现率上 全面领先，率先实现可商用的AI视频生产能力。 AI视频的稳定性非常重要，人物能不能保持一致，画面会不会中途崩坏，生成结果能不能被反复复 现。 这些指标恰恰决定了视频能否进入真实生产。 后来我们能看到，一批中国公司沿着同一条路径继续推进。 字节在 Seedance 体系里不断强化叙事和镜头逻辑，而一些更小创业团队甚至会把视频生成直接嵌进电 商、广告、游戏买量的工作流中。 这些 ...

Core Viewpoint - The article discusses the top 10 recommended papers in the field of end-to-end autonomous driving, highlighting the increasing presence of Li Auto in the discourse surrounding autonomous driving technology and research [2][20][22]. Group 1: Overview of Recommended Papers - The article presents a list of 10 highly recommended papers in the end-to-end autonomous driving domain, compiled from interviews with leading researchers [22][26]. - The papers cover various innovative approaches, including reinforcement learning, vision-language models, and multimodal frameworks [27][29][35][40]. Group 2: Key Innovations and Technologies - The paper "TransDiffuser" introduces an encoder-decoder model for trajectory generation, utilizing multimodal perception information to create diverse and high-quality trajectories [10][42]. - The diffusion model is highlighted for its ability to generate trajectories by learning from noise, significantly improving the model's performance in complex traffic environments [6][7][13][16]. - The architecture of TransDiffuser includes a scene encoder for processing multimodal data and a denoising decoder for trajectory generation [11][12][14]. Group 3: Performance Metrics and Results - TransDiffuser achieved a Predictive Driver Model Score (PDMS) of 94.85 on the NAVSIM benchmark, outperforming existing methods [15][42]. - The model's efficiency is enhanced through the use of ordinary differential equations (ODE) sampling, allowing for rapid trajectory generation [7][13]. Group 4: Future Directions and Challenges - The authors of the papers acknowledge challenges in fine-tuning models and suggest future work could involve integrating reinforcement learning and exploring models like OpenVLA [17][18]. - The article emphasizes the ongoing evolution in the field, with a shift towards more integrated and robust approaches to autonomous driving [70].